


import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
def 							UpperCamelCase_(					_A :Dict					,  _A :Optional[Any]=False	)->		Dict:
			try:
						UpperCamelCase__       =							os.environ[key]
			except KeyError:
						# KEY isn't set, default to `default`.
						UpperCamelCase__       =							default
			else:
						# KEY is set, convert it to True or False.
						try:
									UpperCamelCase__       =							strtobool(_A	)
						except ValueError:
									# More values are supported, but let's keep the message simple.
									raise ValueError(F'''If set, {key} must be yes or no.'''	)
			return _value


__UpperCamelCase     		=							parse_flag_from_env('RUN_SLOW', default=False)
def 							UpperCamelCase_(					_A :Union[str, Any]	)->		str:
			return unittest.skip("Test was skipped"	)(_A	)
def 							UpperCamelCase_(					_A :Tuple	)->		Dict:
			return unittest.skipUnless(_run_slow_tests					,  "test is slow"	)(_A	)
def 							UpperCamelCase_(					_A :Optional[Any]	)->		Dict:
			return unittest.skipUnless(not torch.cuda.is_available()					,  "test requires only a CPU"	)(_A	)
def 							UpperCamelCase_(					_A :List[str]	)->		str:
			return unittest.skipUnless(torch.cuda.is_available()					,  "test requires a GPU"	)(_A	)
def 							UpperCamelCase_(					_A :Union[str, Any]	)->		Tuple:
			return unittest.skipUnless(is_xpu_available()					,  "test requires a XPU"	)(_A	)
def 							UpperCamelCase_(					_A :Optional[int]	)->		Union[str, Any]:
			return unittest.skipUnless(is_mps_available()					,  "test requires a `mps` backend support in `torch`"	)(_A	)
def 							UpperCamelCase_(					_A :List[str]	)->		Dict:
			return unittest.skipUnless(
			    is_transformers_available() and is_datasets_available()					,  "test requires the Hugging Face suite"	)(_A	)
def 							UpperCamelCase_(					_A :Any	)->		Tuple:
			return unittest.skipUnless(is_bnb_available()					,  "test requires the bitsandbytes library"	)(_A	)
def 							UpperCamelCase_(					_A :Tuple	)->		str:
			return unittest.skipUnless(is_tpu_available()					,  "test requires TPU"	)(_A	)
def 							UpperCamelCase_(					_A :List[str]	)->		Dict:
			return unittest.skipUnless(torch.cuda.device_count() == 1					,  "test requires a GPU"	)(_A	)
def 							UpperCamelCase_(					_A :List[str]	)->		Optional[Any]:
			return unittest.skipUnless(torch.xpu.device_count() == 1					,  "test requires a XPU"	)(_A	)
def 							UpperCamelCase_(					_A :Dict	)->		Optional[Any]:
			return unittest.skipUnless(torch.cuda.device_count() > 1					,  "test requires multiple GPUs"	)(_A	)
def 							UpperCamelCase_(					_A :Tuple	)->		Dict:
			return unittest.skipUnless(torch.xpu.device_count() > 1					,  "test requires multiple XPUs"	)(_A	)
def 							UpperCamelCase_(					_A :Optional[int]	)->		str:
			return unittest.skipUnless(is_safetensors_available()					,  "test requires safetensors"	)(_A	)
def 							UpperCamelCase_(					_A :List[Any]	)->		Optional[int]:
			return unittest.skipUnless(is_deepspeed_available()					,  "test requires DeepSpeed"	)(_A	)
def 							UpperCamelCase_(					_A :int	)->		Union[str, Any]:
			return unittest.skipUnless(is_torch_version(">="					,  "1.12.0"	)					,  "test requires torch version >= 1.12.0"	)(_A	)
def 							UpperCamelCase_(					_A :List[Any]=None					,  _A :Optional[int]=None	)->		Optional[Any]:
			if test_case is None:
						return partial(_A					,  version=_A	)
			return unittest.skipUnless(is_torch_version(">="					,  _A	)					,  F'''test requires torch version >= {version}'''	)(_A	)
def 							UpperCamelCase_(					_A :Optional[int]	)->		Any:
			return unittest.skipUnless(is_tensorboard_available()					,  "test requires Tensorboard"	)(_A	)
def 							UpperCamelCase_(					_A :Dict	)->		Tuple:
			return unittest.skipUnless(is_wandb_available()					,  "test requires wandb"	)(_A	)
def 							UpperCamelCase_(					_A :Union[str, Any]	)->		Union[str, Any]:
			return unittest.skipUnless(is_comet_ml_available()					,  "test requires comet_ml"	)(_A	)


__UpperCamelCase     		=							(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def 							UpperCamelCase_(					_A :Dict	)->		Union[str, Any]:
			return unittest.skipUnless(
			    _atleast_one_tracker_available					,  "test requires at least one tracker to be available and for `comet_ml` to not be installed"					,  )(_A	)







class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Dict 			=		True




			@classmethod
			def 					snake_case__					(				cls  ):

						'''simple docstring'''


						UpperCamelCase__       =							tempfile.mkdtemp()




			@classmethod
			def 					snake_case__					(				cls  ):

						'''simple docstring'''


						if os.path.exists(cls.tmpdir  ):
									shutil.rmtree(cls.tmpdir  )







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						if self.clear_on_setup:
									for path in Path(self.tmpdir  ).glob("**/*"  ):
												if path.is_file():
															path.unlink()
												elif path.is_dir():
															shutil.rmtree(snake_case  )







class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().tearDown()
						# Reset the state of the AcceleratorState singleton.
						AcceleratorState._reset_state()
						PartialState._reset_state()







class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							mocks if isinstance(snake_case   ,    (tuple, list)  ) else [mocks]
						for m in self.mocks:
									m.start()
									self.addCleanup(m.stop  )



def 							UpperCamelCase_(					_A :str	)->		Union[str, Any]:
			UpperCamelCase__       =							AcceleratorState()
			UpperCamelCase__       =							tensor[None].clone().to(state.device	)
			UpperCamelCase__       =							gather(_A	).cpu()
			UpperCamelCase__       =							tensor[0].cpu()
			for i in range(tensors.shape[0]	):
						if not torch.equal(tensors[i]					,  _A	):
									return False
			return True







class 							lowerCamelCase__						:






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							returncode
						UpperCamelCase__       =							stdout
						UpperCamelCase__       =							stderr



async def 							UpperCamelCase_(					_A :Optional[Any]					,  _A :Any	)->		List[Any]:
			while True:
						UpperCamelCase__       =							await stream.readline()
						if line:
									callback(_A	)
						else:
									break
async def 							UpperCamelCase_(					_A :List[str]					,  _A :List[str]=None					,  _A :str=None					,  _A :Optional[int]=None					,  _A :Dict=False					,  _A :Tuple=False	)->		_RunOutput:
			if echo:
						print("\nRunning: "					,  " ".join(_A	)	)

			UpperCamelCase__       =							await asyncio.create_subprocess_exec(
			    cmd[0]					,  *cmd[1:]					,  stdin=_A					,  stdout=asyncio.subprocess.PIPE					,  stderr=asyncio.subprocess.PIPE					,  env=_A					,  )

			# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
			# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
			#
			# If it starts hanging, will need to switch to the following code. The problem is that no data
			# will be seen until it's done and if it hangs for example there will be no debug info.
			# out, err = await p.communicate()
			# return _RunOutput(p.returncode, out, err)

			UpperCamelCase__       =							[]
			UpperCamelCase__       =							[]

			def tee(_A :Optional[int]					,  _A :Any					,  _A :Any					,  _A :Any=""	):
						UpperCamelCase__       =							line.decode("utf-8"	).rstrip()
						sink.append(_A	)
						if not quiet:
									print(_A					,  _A					,  file=_A	)

    # XXX: the timeout doesn't seem to make any difference here
			await asyncio.wait(
			    [
			        asyncio.create_task(_read_stream(p.stdout					,  lambda _A	: tee(_A					,  _A					,  sys.stdout					,  label="stdout:"	)	)	),
			        asyncio.create_task(_read_stream(p.stderr					,  lambda _A	: tee(_A					,  _A					,  sys.stderr					,  label="stderr:"	)	)	),
			    ]					,  timeout=_A					,  )
			return _RunOutput(await p.wait()					,  _A					,  _A	)
def 							UpperCamelCase_(					_A :Optional[int]					,  _A :Dict=None					,  _A :Union[str, Any]=None					,  _A :Tuple=1_80					,  _A :Union[str, Any]=False					,  _A :Optional[int]=True	)->		_RunOutput:
			UpperCamelCase__       =							asyncio.get_event_loop()
			UpperCamelCase__       =							loop.run_until_complete(
			    _stream_subprocess(_A					,  env=_A					,  stdin=_A					,  timeout=_A					,  quiet=_A					,  echo=_A	)	)

			UpperCamelCase__       =							" ".join(_A	)
			if result.returncode > 0:
						UpperCamelCase__       =							"\n".join(result.stderr	)
						raise RuntimeError(
						    F'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
						    F'''The combined stderr from workers follows:\n{stderr}'''	)

			return result







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			pass



def 							UpperCamelCase_(					_A :List[str]					,  _A :Union[str, Any]=False	)->		List[str]:
			try:
						UpperCamelCase__       =							subprocess.check_output(_A					,  stderr=subprocess.STDOUT	)
						if return_stdout:
									if hasattr(_A					,  "decode"	):
												UpperCamelCase__       =							output.decode("utf-8"	)
									return output
			except subprocess.CalledProcessError as e:
						raise SubprocessCallException(
						    F'''Command `{' '.join(_A	)}` failed with the following error:\n\n{e.output.decode()}'''	) from e






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class 							lowerCamelCase__						(				UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       List[str] 			=		MvpTokenizer
			_UpperCamelCase		:       Any 			=		MvpTokenizerFast
			_UpperCamelCase		:       List[str] 			=		True
			_UpperCamelCase		:       Dict 			=		filter_roberta_detectors




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().setUp()
						UpperCamelCase__       =							[
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "\u0120",
						    "\u0120l",
						    "\u0120n",
						    "\u0120lo",
						    "\u0120low",
						    "er",
						    "\u0120lowest",
						    "\u0120newer",
						    "\u0120wider",
						    "<unk>",
						]
						UpperCamelCase__       =							dict(zip(snake_case   ,    range(len(snake_case  )  )  )  )
						UpperCamelCase__       =							["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
						UpperCamelCase__       =							{"unk_token": "<unk>"}

						UpperCamelCase__       =							os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES["vocab_file"]  )
						UpperCamelCase__       =							os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES["merges_file"]  )
						with open(self.vocab_file   ,    "w"   ,    encoding="utf-8"  ) as fp:
									fp.write(json.dumps(snake_case  ) + "\n"  )
						with open(self.merges_file   ,    "w"   ,    encoding="utf-8"  ) as fp:
									fp.write("\n".join(snake_case  )  )




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						kwargs.update(self.special_tokens_map  )
						return self.tokenizer_class.from_pretrained(self.tmpdirname   ,    **snake_case  )




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						kwargs.update(self.special_tokens_map  )
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,    **snake_case  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						return "lower newer", "lower newer"




			@cached_property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return MvpTokenizer.from_pretrained("RUCAIBox/mvp"  )




			@cached_property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							["A long paragraph for summarization.", "Another paragraph for summarization."]
						UpperCamelCase__       =							[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(snake_case   ,    max_length=len(snake_case  )   ,    padding=snake_case   ,    return_tensors="pt"  )
									self.assertIsInstance(snake_case   ,    snake_case  )

									self.assertEqual((2, 9)   ,    batch.input_ids.shape  )
									self.assertEqual((2, 9)   ,    batch.attention_mask.shape  )
									UpperCamelCase__       =							batch.input_ids.tolist()[0]
									self.assertListEqual(snake_case   ,    snake_case  )
									# Test that special tokens are reset




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							["A long paragraph for summarization.", "Another paragraph for summarization."]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(snake_case   ,    padding=snake_case   ,    return_tensors="pt"  )
									# check if input_ids are returned and no labels
									self.assertIn("input_ids"   ,    snake_case  )
									self.assertIn("attention_mask"   ,    snake_case  )
									self.assertNotIn("labels"   ,    snake_case  )
									self.assertNotIn("decoder_attention_mask"   ,    snake_case  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							[
						    "Summary of the text.",
						    "Another summary.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(text_target=snake_case   ,    max_length=32   ,    padding="max_length"   ,    return_tensors="pt"  )
									self.assertEqual(32   ,    targets["input_ids"].shape[1]  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(
									    ["I am a small frog" * 1024, "I am a small frog"]   ,    padding=snake_case   ,    truncation=snake_case   ,    return_tensors="pt"  )
									self.assertIsInstance(snake_case   ,    snake_case  )
									self.assertEqual(batch.input_ids.shape   ,    (2, 1024)  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							["A long paragraph for summarization."]
						UpperCamelCase__       =							[
						    "Summary of the text.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(snake_case   ,    text_target=snake_case   ,    return_tensors="pt"  )
									UpperCamelCase__       =							inputs["input_ids"]
									UpperCamelCase__       =							inputs["labels"]
									self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
									self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
									self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
									self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						pass







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
												UpperCamelCase__       =							self.rust_tokenizer_class.from_pretrained(snake_case   ,    **snake_case  )
												UpperCamelCase__       =							self.tokenizer_class.from_pretrained(snake_case   ,    **snake_case  )
												UpperCamelCase__       =							"A, <mask> AllenNLP sentence."
												UpperCamelCase__       =							tokenizer_r.encode_plus(snake_case   ,    add_special_tokens=snake_case   ,    return_token_type_ids=snake_case  )
												UpperCamelCase__       =							tokenizer_p.encode_plus(snake_case   ,    add_special_tokens=snake_case   ,    return_token_type_ids=snake_case  )

												# token_type_ids should put 0 everywhere
												self.assertEqual(sum(tokens_r["token_type_ids"]  )   ,    sum(tokens_p["token_type_ids"]  )  )

												# attention_mask should put 1 everywhere, so sum over length should be 1
												self.assertEqual(
												    sum(tokens_r["attention_mask"]  ) / len(tokens_r["attention_mask"]  )   ,    sum(tokens_p["attention_mask"]  ) / len(tokens_p["attention_mask"]  )   ,    )

												UpperCamelCase__       =							tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]  )
												UpperCamelCase__       =							tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]  )

												# Rust correctly handles the space before the mask while python doesnt
												self.assertSequenceEqual(tokens_p["input_ids"]   ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]  )
												self.assertSequenceEqual(tokens_r["input_ids"]   ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]  )

												self.assertSequenceEqual(
												    snake_case   ,    ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )
												self.assertSequenceEqual(
												    snake_case   ,    ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )


